A Physics Informed Digital Twin Framework for Demand Side Optimization in Smart Greenhouses

Abstract

Modern smart greenhouses require sophisticated energy management strategies to balance crop comfort with increasing energy costs and grid volatility. While traditional control methods rely on reactive thresholding, they often fail to account for the complex and future energy availability. This paper presents a Physics Informed Digital Twin (PIDT) framework specifically designed for demand side optimization in smart greenhouses. The proposed architecture integrates a physical layer of sensors and actuators with a cyber layer containing discrete time state space models. By embedding fundamental heat and mass balance equations directly into the digital twin's core, the framework enables real-time synchronization and predictive capabilities for temperature, humidity, and CO₂ concentration. A demand side management (DSM) structure that utilizes this digital twin to evaluate energy shifting strategies involving PV and battery storage. This work establishes the structural and mathematical foundation necessary for deploying advanced model based control in complex agricultural environments. Results from the framework's mathematical validation indicate its suitability for real time applications and its potential to serve as a high accuracy virtual testbed for optimizing energy use while maintaining strict crop comfort constraints.